RLProph: a dynamic programming based reinforcement learning approach for optimal routing in opportunistic IoT networks

Sharma, Deepak Kumar; Rodrigues, Joel J. P. C.; Vashishth, Vidushi; Khanna, Anirudh; Chhabra, Anshuman

doi:10.1007/s11276-020-02331-1

Cited by 45 publications

(28 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The performance of the proposed RFCSec protocol is compared against that of the MLProph, 21 CAML, 22 and RLProph 27 protocols under varying number of nodes, percentage of malicious nodes in network, time‐to‐live (TTL) of packets, and buffer size of nodes. The considered performance metrics are average number of packets dropped, probability of message delivery, average delay and number of correct packets delivered to the destination.…”

Section: Simulation Resultsmentioning

confidence: 99%

Random forest classifier‐based safe and reliable routing for opportunistic IoT networks

Kandhoul

Dhurandher

Woungang

2020

Int J Communication

View full text Add to dashboard Cite

SummaryDesigning a safe and reliable way for communicating the messages among the devices and humans forming the Opportunistic Internet of Things network (OppIoT) has been a challenge since the broadcast mode of message sharing is used. To contribute toward addressing such challenge, this paper proposes a Random Forest Classifier (RFC)‐based safe and reliable routing protocol for OppIoT (called RFCSec) which ensures space efficiency, hash‐based message integrity, and high packet delivery, simultaneously protecting the network against safety threats viz. packet collusion, hypernova, supernova, and wormhole attacks. The proposed RFCSec scheme is composed of two phases. In the first one, the RFC is trained on real data trace, and based on the output of this training, the second phase consists in classifying the encountered nodes of a given node as belonging to one of the output classes of nodes based on their past behavior in the network. This helps in proactively isolating the malicious nodes from participating in the routing process and encourages the participation of the ones with good message forwarding behavior, low packet dropping rate, high buffer availability, and a higher probability of delivering the messages in the past. Simulation results using the ONE simulator show that the proposed RFCSec secure routing scheme is superior to the MLProph, RLProph, and CAML routing protocols, chosen as benchmarks, in terms of legitimate packet delivery, probability of message delivery, count of dropped messages, and latency in packet delivery. The out‐of‐bag error obtained is also minimal

show abstract

Section: Simulation Resultsmentioning

confidence: 99%

Random forest classifier‐based safe and reliable routing for opportunistic IoT networks

Kandhoul

Dhurandher

Woungang

2020

Int J Communication

View full text Add to dashboard Cite

show abstract

“…Referred publications Markov decision process [12,23,24,37,64,70,75,84,96,100,101,104,127,130,133,138,144,153,165,167,170,177,188,191,199], [203, 207, 211, 212, 214, 217, 220, 231, 252, 256-259, 263, 264, 272, 274, 281, 291, 309, 313, 320, 340, 343, 346], [369][370][371][372][373][374][375][376] Multiarmed bandit [61,66,102,198,351,377,378] Dynamic programming [16,19,27,52,68,70,84,90,93,…”

Section: Approachmentioning

confidence: 99%

The Applicability of Reinforcement Learning Methods in the Development of Industry 4.0 Applications

2021

View full text Add to dashboard Cite

Reinforcement learning (RL) methods can successfully solve complex optimization problems. Our article gives a systematic overview of major types of RL methods, their applications at the field of Industry 4.0 solutions, and it provides methodological guidelines to determine the right approach that can be fitted better to the different problems, and moreover, it can be a point of reference for R&D projects and further researches.

show abstract

“…The application of nature-inspired algorithms in engineering and the sciences has resulted in large communication benefits. Algorithms based on swarm intelligence like the ant colony optimization [18], grey wolf optimizer [7], firefly algorithm [9] are capable of managing complex problems with easy rules. In IOT, swarm intelligence based algorithms use the foraging practices of animals in order to solve complex optimization problems [19].…”

Section: In This Work We Propose a Fuzzy Logic Based Decisionmentioning

confidence: 99%

Optimized Resource Allocation in IoT using Fuzzy Logic and Bio-Inspired Algorithms

Sharma

Mishra

Singh

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

IoT smart devices are a confluence of microprocessors, sensors, power source and transceiver modules to effectively sense, communicate and transfer data. Energy efficiency is a key governing value of the network performance of smart devices in distributed IoT networks.Low and discrete power and limited amount of memory and finite amount of resources form some major bottlenecks in the workflow.Dynamic load balancing, reliability and flexibility are heavily relied upon by cloud computing for its accessibility.Resources are dynamically provided to the end client in an as-come on-demand fashion with the global network that is the Internet. Proportionally the need for services is increasing at a rate that is astonishing compared to any other forms of development. Load balancing seems a major challenge faced due to the architecture and the modular nature of our cloud environment. Loads need to be distributed dynamically to all the nodes. In this paper, we have introduced a technique that combines fuzzy logic with various nature inspired algorithms - grey wolf algorithm and firefly algorithm in order to effectively balance the load in a network of IoT devices. The performances of various nature inspired algorithms are compared with a brute force approach on the basis of energy efficiency, network lifetime maximization, node failure rate and packet delivery ratio.

show abstract

RLProph: a dynamic programming based reinforcement learning approach for optimal routing in opportunistic IoT networks

Cited by 45 publications

References 28 publications

Random forest classifier‐based safe and reliable routing for opportunistic IoT networks

Random forest classifier‐based safe and reliable routing for opportunistic IoT networks

The Applicability of Reinforcement Learning Methods in the Development of Industry 4.0 Applications

Optimized Resource Allocation in IoT using Fuzzy Logic and Bio-Inspired Algorithms

Contact Info

Product

Resources

About